Trigger-Based Data Ingestion for Machine Learning Using Edge Device

Embodiments pertain to machine learning. Some embodiments relate to trigger-based data ingestion for machine learning inference technology.

Machine learning inference technology may be performed with sensor data obtained from edge devices. However, in order to obtain relevant data, (e.g., data associated with certain conditions) a large amount of sensor data might need to be processed. This might not be possible with a resource-constrained (e.g., in terms of memory and power) edge device. Furthermore, processing the obtained data to determine if data of interest has been captured may be cumbersome. Techniques for optimizing the process of obtaining sensor data from the edge device may be desirable.

The following description and the drawings sufficiently illustrate specific embodiments to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. Portions and features of some embodiments may be included in, or substituted for, those of other embodiments. Embodiments set forth in the claims encompass all available equivalents of those claims.

As discussed above, techniques for optimizing the process of obtaining sensor data from an edge device may be desirable. An edge device may include a thin device with limited (e.g., compared to a server or a desktop computer) processing hardware, memory hardware, battery power, and/or network interface capabilities. For example, the edge device may have less than a threshold amount of processing hardware, memory hardware, battery power, and/or network interface capabilities. The edge device may be limited by a processing threshold, a memory threshold, a battery power threshold, and/or a network interface threshold. The processing threshold may include the processing hardware being a processing unit (e.g., a central processing unit (CPU)) with less than 1 gigahertz (GHz) clock speed or a limited number of cores (e.g., less than 4 cores). The memory threshold may include the memory hardware may having less 1 gigabyte (GB) of random-access memory (RAM) and/or less than 8 GB of storage. The battery power threshold may be the battery life being less than 4 hours under continuous operation. The network interface threshold may be the edge device having a maximum data transfer rate of less than 100 megabits per second (Mbps) or limited to 2.4 GHz Wi-Fi® connectivity. An edge device may be a single device or may include multiple devices. For example, an edge device may be a thin computer used to capture sensor data in the field in an agricultural, military, or similar setting. Alternatively, the edge device may be an Internet of Things (IoT) device installed in an appliance.

According to some implementations, an edge device that includes processing circuitry and memory stores a representation of a trigger condition for transmitting data. The trigger condition may be, for example, a temperature read by a temperature sensor exceeding 100 Celsius, a pressure read by a pressure sensor exceeding 800 millimeters of mercury (mmHg) or falling below 700 mmHg, a velocity exceeding 100 kilometers per hour, or the like. Alternatively, the trigger condition may be a mathematical function of the sensor data (e.g., a rate of change of the sensor data, a quotient of the pressure divided by the temperature, or the like). The edge device accesses streaming sensor data, for example, using sensor(s) connected to the edge device or included in the edge device. The edge device determines, based on the streaming sensor data, that the trigger condition is met, for example, by continuously reading the streaming sensor data and comparing the streaming sensor data to the trigger condition. Based on determining that the trigger condition is met, the edge device transmits the streaming sensor data to a computing device. The computing device may be a server, a desktop computer, or a laptop computer. The computing device may include multiple computing devices, for example, in a computing studio or a server farm.

Aspects of the present technology may be implemented as part of a computer system. The computer system may be one physical machine, or may be distributed among multiple physical machines, such as by role or function, or by process thread in the case of a cloud computing distributed model. In various embodiments, aspects of the technology may be configured to run in virtual machines that in turn are executed on one or more physical machines. It will be understood by persons of skill in the art that features of the technology may be realized by a variety of different suitable machine implementations.

The system includes various engines, each of which is constructed, programmed, configured, or otherwise adapted, to carry out a function or set of functions. The term engine as used herein means a tangible device, component, or arrangement of components implemented using hardware, such as by an application specific integrated circuit (ASIC) or field-programmable gate array (FPGA), for example, or as a combination of hardware and software, such as by a processor-based computing platform and a set of program instructions that transform the computing platform into a special-purpose device to implement the particular functionality. An engine may also be implemented as a combination of the two, with certain functions facilitated by hardware alone, and other functions facilitated by a combination of hardware and software.

In an example, the software may reside in executable or non-executable form on a tangible machine-readable storage medium. Software residing in non-executable form may be compiled, translated, or otherwise converted to an executable form prior to, or during, runtime. In an example, the software, when executed by the underlying hardware of the engine, causes the hardware to perform the specified operations. Accordingly, an engine is physically constructed, or specifically configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operations described herein in connection with that engine.

Considering examples in which engines are temporarily configured, each of the engines may be instantiated at different moments in time. For example, where the engines comprise a general-purpose hardware processor core configured using software, the general-purpose hardware processor core may be configured as respective different engines at different times. Software may accordingly configure a hardware processor core, for example, to constitute a particular engine at one instance of time and to constitute a different engine at a different instance of time.

In certain implementations, at least a portion, and in some cases, all, of an engine may be executed on the processor(s) of one or more computers that execute an operating system, system programs, and application programs, while also implementing the engine using multitasking, multithreading, distributed (e.g., cluster, peer-peer, cloud, etc.) processing where appropriate, or other such techniques. Accordingly, each engine may be realized in a variety of suitable configurations, and should generally not be limited to any particular implementation exemplified herein, unless such limitations are expressly called out.

In addition, an engine may itself be composed of more than one sub-engines, each of which may be regarded as an engine in its own right. Moreover, in the embodiments described herein, each of the various engines corresponds to a defined functionality; however, it should be understood that in other contemplated embodiments, each functionality may be distributed to more than one engine. Likewise, in other contemplated embodiments, multiple defined functionalities may be implemented by a single engine that performs those multiple functions, possibly alongside other functions, or distributed differently among a set of engines than specifically illustrated in the examples herein.

As used herein, the term “model” encompasses its plain and ordinary meaning. A model may include, among other things, one or more engines which receive an input and compute an output based on the input. The output may be a classification. For example, an image file may be classified as depicting a cat or not depicting a cat. Alternatively, the image file may be assigned a numeric score indicating a likelihood whether the image file depicts the cat, and image files with a score exceeding a threshold (e.g., 0.9 or 0.95) may be determined to depict the cat.

This document may reference a specific number of things (e.g., “six mobile devices”). Unless explicitly set forth otherwise, the numbers provided are examples only and may be replaced with any positive integer, integer or real number, as would make sense for a given situation. For example, “six mobile devices” may, in alternative embodiments, include any positive integer number of mobile devices. Unless otherwise mentioned, an object referred to in singular form (e.g., “a computer” or “the computer”) may include one or multiple objects (e.g., “the computer” may refer to one or multiple computers).

1 FIG. illustrates the training and use of a machine-learning program, according to some example embodiments. In some example embodiments, machine-learning programs (MLPs), also referred to as machine-learning algorithms or tools, are utilized to perform operations associated with machine learning tasks, such as image recognition or machine translation.

112 120 Machine learning is a field of study that gives computers the ability to perform certain tasks without being explicitly programmed to perform those tasks. In traditional computing, a programmer would encode instructions (e.g., to solve a quadratic equation using the quadratic formula), and the computer would perform those exact instructions. In contrast, in machine learning, a computer could be provided with examples of images of elephants and be trained to determine which images have and lack depictions of elephants, without the programmer encoding explicit instructions as to how to identify an elephant. Machine learning explores the study and construction of algorithms, also referred to herein as tools, which may learn from existing data and make predictions about new data. Such machine-learning tools operate by building a model from example training datain order to make data-driven predictions or decisions expressed as outputs or assessments. Although example embodiments are presented with respect to a few machine-learning tools, the principles presented herein may be applied to other machine-learning tools.

In some example embodiments, different machine-learning tools may be used. For example, Logistic Regression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN), matrix factorization, and Support Vector Machines (SVM) tools may be used for classifying or scoring job postings.

112 102 Two common types of problems in machine learning are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (for example, is this object an apple or an orange). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number). The machine-learning algorithms utilize the training datato find correlations among identified featuresthat affect the outcome.

102 120 102 The machine-learning algorithms utilize featuresfor analyzing the data to generate assessments. A featureis an individual measurable property of a phenomenon being observed. The concept of a feature is related to that of an explanatory variable used in statistical techniques such as linear regression. Choosing informative, discriminating, and independent features is important for effective operation of the MLP in pattern recognition, classification, and regression. Features may be of different types, such as numeric features, strings, and graphs.

102 103 104 105 106 107 108 109 110 In one example embodiment, the featuresmay be of different types and may include one or more of words of the message, message concepts, communication history, past user behavior, subject of the message, other message attributes, sender, and user data.

112 102 120 112 102 The machine-learning algorithms utilize the training datato find correlations among the identified featuresthat affect the outcome or assessment. In some example embodiments, the training dataincludes labeled data, which is known data for one or more identified featuresand one or more outcomes, such as detecting communication patterns, detecting the meaning of the message, generating a summary of the message, detecting action items in the message, detecting urgency in the message, detecting a relationship of the user to the sender, calculating score attributes, calculating message scores, etc.

112 102 114 102 112 116 With the training dataand the identified features, the machine-learning tool is trained at operation. The machine-learning tool appraises the value of the featuresas they correlate to the training data. The result of the training is the trained machine-learning program.

116 118 116 116 120 When the machine-learning programis used to perform an assessment, new datais provided as an input to the trained machine-learning program, and the machine-learning programgenerates the assessmentas output. For example, when a message is checked for an action item, the machine-learning program utilizes the message content and message metadata to determine if there is a request for an action in the message.

Machine learning techniques train models to accurately make predictions on data fed into the models (e.g., what was said by a user in a given utterance; whether a noun is a person, place, or thing; what the weather will be like tomorrow). During a learning phase, the models are developed against a training dataset of inputs to optimize the models to correctly predict the output for a given input. Generally, the learning phase may be supervised, semi-supervised, or unsupervised; indicating a decreasing level to which the “correct” outputs are provided in correspondence to the training inputs. In a supervised learning phase, all of the outputs are provided to the model and the model is directed to develop a general rule or algorithm that maps the input to the output. In contrast, in an unsupervised learning phase, the desired output is not provided for the inputs so that the model may develop its own rules to discover relationships within the training dataset. In a semi-supervised learning phase, an incompletely labeled training set is provided, with some of the outputs known and some unknown for the training dataset.

Models may be run against a training dataset for several epochs (e.g., iterations), in which the training dataset is repeatedly fed into the model to refine its results. For example, in a supervised learning phase, a model is developed to predict the output for a given set of inputs, and is evaluated over several epochs to more reliably provide the output that is specified as corresponding to the given input for the greatest number of inputs for the training dataset. In another example, for an unsupervised learning phase, a model is developed to cluster the dataset into n groups, and is evaluated over several epochs as to how consistently it places a given input into a given group and how reliably it produces the n desired clusters across each epoch.

Once an epoch is run, the models are evaluated and the values of their variables are adjusted to attempt to better refine the model in an iterative fashion. In various aspects, the evaluations are biased against false negatives, biased against false positives, or evenly biased with respect to the overall accuracy of the model. The values may be adjusted in several ways depending on the machine learning technique used. For example, in a genetic or evolutionary algorithm, the values for the models that are most successful in predicting the desired outputs are used to develop values for models to use during the subsequent epoch, which may include random variation/mutation to provide additional data points. One of ordinary skill in the art will be familiar with several other machine learning algorithms that may be applied with the present disclosure, including linear regression, random forests, decision tree learning, neural networks, deep neural networks, etc.

th Each model develops a rule or algorithm over several epochs by varying the values of one or more variables affecting the inputs to more closely map to a desired result, but as the training dataset may be varied, and is preferably very large, perfect accuracy and precision may not be achievable. A number of epochs that make up a learning phase, therefore, may be set as a given number of trials or a fixed time/computing budget, or may be terminated before that number/budget is reached when the accuracy of a given model is high enough or low enough or an accuracy plateau has been reached. For example, if the training phase is designed to run n epochs and produce a model with at least 95% accuracy, and such a model is produced before the nepoch, the learning phase may end early and use the produced model satisfying the end-goal accuracy threshold. Similarly, if a given model is inaccurate enough to satisfy a random chance threshold (e.g., the model is only 55% accurate in determining true/false outputs for given inputs), the learning phase for that model may be terminated early, although other models in the learning phase may continue training. Similarly, when a given model continues to provide similar accuracy or vacillate in its results across multiple epochs—having reached a performance plateau—the learning phase for the given model may terminate before the epoch number/computing budget is reached.

Once the learning phase is complete, the models are finalized. In some example embodiments, models that are finalized are evaluated against testing criteria. In a first example, a testing dataset that includes known outputs for its inputs is fed into the finalized models to determine an accuracy of the model in handling data that it has not been trained on. In a second example, a false positive rate or false negative rate may be used to evaluate the models after finalization. In a third example, a delineation between data clusters is used to select a model that produces the clearest bounds for its clusters of data.

2 FIG. 204 204 202 206 206 208 208 206 204 illustrates an example neural network, in accordance with some embodiments. As shown, the neural networkreceives, as input, source domain data. The input is passed through a plurality of layersto arrive at an output. Each layerincludes multiple neurons. The neuronsreceive input from neurons of a previous layer and apply weights to the values received from those neurons in order to generate a neuron output. The neuron outputs from the final layerare combined to generate the output of the neural network.

2 FIG. 206 1 2 i 1 2 i-1 As illustrated at the bottom of, the input is a vector x. The input is passed through multiple layers, where weights W, W, . . . , Ware applied to the input to each layer to arrive at f(x), f(x), . . . , f(x), until finally the output f(x) is computed.

204 208 208 208 208 208 204 208 In some example embodiments, the neural network(e.g., deep learning, deep convolutional, or recurrent neural network) comprises a series of neurons, such as Long Short Term Memory (LSTM) nodes, arranged into a network. A neuronis an architectural element used in data processing and artificial intelligence, particularly machine learning, which includes memory that may determine when to “remember” and when to “forget” values held in that memory based on the weights of inputs provided to the given neuron. Each of the neuronsused herein are configured to accept a predefined number of inputs from other neuronsin the neural networkto provide relational and sub-relational outputs for the content of the frames being analyzed. Individual neuronsmay be chained together and/or organized into tree structures in various configurations of neural networks to provide interactions and relationship learning modeling for how each of the frames in an utterance are related to one another.

For example, an LSTM node serving as a neuron includes several gates to handle input vectors (e.g., phonemes from an utterance), a memory cell, and an output vector (e.g., contextual representation). The input gate and output gate control the information flowing into and out of the memory cell, respectively, whereas forget gates optionally remove information from the memory cell based on the inputs from linked cells earlier in the neural network. Weights and bias vectors for the various gates are adjusted over the course of a training phase, and once the training phase is complete, those weights and biases are finalized for normal operation. One of skill in the art will appreciate that neurons and neural networks may be constructed programmatically (e.g., via software instructions) or via specialized hardware linking each neuron to form the neural network.

Neural networks utilize features for analyzing the data to generate assessments (e.g., recognize units of speech). A feature is an individual measurable property of a phenomenon being observed. The concept of feature is related to that of an explanatory variable used in statistical techniques such as linear regression. Further, deep features represent the output of nodes in hidden layers of the deep neural network.

A neural network, sometimes referred to as an artificial neural network, is a computing system/apparatus based on consideration of biological neural networks of animal brains. Such systems/apparatus progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images. A neural network is based on a collection of connected units called neurons, where each connection, called a synapse, between neurons can transmit a unidirectional signal with an activating strength that varies with the strength of the connection. The receiving neuron can activate and propagate a signal to downstream neurons connected to it, typically based on whether the combined incoming signals, which are from potentially many transmitting neurons, are of sufficient strength, where strength is a parameter.

A deep neural network (DNN) is a stacked neural network, which is composed of multiple layers. The layers are composed of nodes, which are locations where computation occurs, loosely patterned on a neuron in the human brain, which fires when it encounters sufficient stimuli. A node combines input from the data with a set of coefficients, or weights, that either amplify or dampen that input, which assigns significance to inputs for the task the algorithm is trying to learn. These input-weight products are summed, and the sum is passed through what is called a node's activation function, to determine whether and to what extent that signal progresses further through the network to affect the ultimate outcome. A DNN uses a cascade of many layers of non-linear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Higher-level features are derived from lower-level features to form a hierarchical representation. The layers following the input layer may be convolution layers that produce feature maps that are filtering results of the inputs and are used by the next convolution layer.

In training of a DNN architecture, a regression, which is structured as a set of statistical processes for estimating the relationships among variables, can include a minimization of a cost function. The cost function may be implemented as a function to return a number representing how well the neural network performed in mapping training examples to correct output. In training, if the cost function value is not within a pre-determined range, based on the known training images, backpropagation is used, where backpropagation is a common method of training artificial neural networks that are used with an optimization method such as a stochastic gradient descent (SGD) method.

Use of backpropagation can include propagation and weight update. When an input is presented to the neural network, it is propagated forward through the neural network, layer by layer, until it reaches the output layer. The output of the neural network is then compared to the desired output, using the cost function, and an error value is calculated for each of the nodes in the output layer. The error values are propagated backwards, starting from the output, until each node has an associated error value which roughly represents its contribution to the original output. Backpropagation can use these error values to calculate the gradient of the cost function with respect to the weights in the neural network. The calculated gradient is fed to the selected optimization method to update the weights to attempt to minimize the cost function.

3 FIG. 302 304 304 306 304 306 304 304 308 310 308 312 314 312 314 illustrates the training of an image recognition machine learning program, in accordance with some embodiments. The machine learning program may be implemented at one or more computing machines. Blockillustrates a training set, which includes multiple classes. Each classincludes multiple imagesassociated with the class. Each classmay correspond to a type of object in the image(e.g., a digit 0-9, a man or a woman, a cat or a dog, etc.). In one example, the machine learning program is trained to recognize images of various persons (i.e., to map a photograph of a person to the person's name), and each classcorresponds to each person, with each individual classcorresponding to an individual person (e.g., one class corresponds to Alyssa P. Hacker, one class corresponds to Ben Bitdiddle, etc.). At blockthe machine learning program is trained, for example, using a deep neural network. At block, the trained classifier (e.g., the trained deep neural network), generated by the training of block, receives an input image, and at blockthe image is recognized. For example, if the imageis a photograph of Alyssa P. Hacker, the classifier recognizes the image as corresponding to Alyssa P. Hacker at block. The classifier may include a DNN, as illustrated by the circle with the circular arrows.

3 FIG. 302 304 illustrates the training of a classifier, according to some example embodiments. A machine learning algorithm is designed for recognizing faces, and a training setincludes data that maps a sample to a class(e.g., a class includes all the images of purses). The classes may also be referred to as labels. Although implementations presented herein are presented with reference to object recognition, the same principles may be applied to train machine-learning programs used for recognizing any type of items.

302 306 304 306 308 310 The training setincludes a plurality of imagesfor each class(e.g., image), and each image is associated with one of the categories to be recognized (e.g., a class). The machine learning program is trainedwith the training data to generate a classifieroperable to recognize images. In some example embodiments, the machine learning program is a DNN.

312 310 312 314 312 When an input imageis to be recognized, the classifieranalyzes the input imageto identify the class (e.g., class) corresponding to the input image.

4 FIG. 402 414 406 413 402 illustrates a convolutional neural network, according to some example embodiments. Training a classifier of the convolutional neural network may be accomplished with feature extraction layersand classifier layer. Each image is analyzed in sequence by a plurality of layers-in the feature-extraction layers.

With the development of deep convolutional neural networks, the focus in face recognition has been to learn a good face embedding-based classifier, in which faces of the same person are close to each other, and faces of different persons are far away from each other. For example, the verification task with the LFW (Labeled Faces in the Wild) dataset has been often used for face verification.

Many face identification tasks (e.g., MegaFace and LFW) are based on a similarity comparison between the images in the gallery set and the query set, which is essentially a K-nearest-neighborhood (KNN) method to estimate the person's identity. In the ideal case, there is a good face feature extractor (inter-class distance is always larger than the intra-class distance), and the KNN method is adequate to estimate the person's identity.

Feature extraction is a process to reduce the amount of resources required to describe a large set of data. When performing analysis of complex data, one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computational power, and it may cause a classification algorithm to overfit to training samples and generalize poorly to new samples. Feature extraction is a general term describing methods of constructing combinations of variables to get around these large data-set problems while still describing the data with sufficient accuracy for the desired purpose.

In some example embodiments, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps. Further, feature extraction is related to dimensionality reduction, such as reducing large vectors (sometimes with very sparse data) to smaller vectors capturing the same, or similar, amount of information.

414 4 FIG. Determining a subset of the initial features is called feature selection. The selected features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data. DNN utilizes a stack of layers, where each layer performs a function. For example, the layer could be a convolution, a non-linear transform, the calculation of an average, etc. Eventually this DNN produces outputs by classifier. In, the data travels from left to right and the features are extracted. The goal of training the neural network is to find the parameters of all the layers that make them adequate for the desired task.

4 FIG. 406 407 413 As shown in, a “stride of 4” filter is applied at layer, and max pooling is applied at layers-. The stride controls how the filter convolves around the input volume. “Stride of 4” refers to the filter convolving around the input volume four units at a time. Max pooling refers to down-sampling by selecting the maximum value in each max pooled region.

In some example embodiments, the structure of each layer is predefined. For example, a convolution layer may contain small convolution kernels and their respective convolution parameters, and a summation layer may calculate the sum, or the weighted sum, of two pixels of the input image. Training assists in defining the weight coefficients for the summation.

One way to improve the performance of DNNs is to identify newer structures for the feature-extraction layers, and another way is by improving the way the parameters are identified at the different layers for accomplishing a desired task. The challenge is that for a typical neural network, there may be millions of parameters to be optimized. Trying to optimize all these parameters from scratch may take hours, days, or even weeks, depending on the amount of computing resources available and the amount of data in the training set.

5 FIG. 5 FIG. 500 500 500 502 500 500 500 500 illustrates a circuit block diagram of a computing machinein accordance with some embodiments. In some embodiments, components of the computing machinemay store or be integrated into other components shown in the circuit block diagram of. For example, portions of the computing machinemay reside in the processorand may be referred to as “processing circuitry.” Processing circuitry may include processing hardware, for example, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and the like. In alternative embodiments, the computing machinemay operate as a standalone device or may be connected (e.g., networked) to other computers. In a networked deployment, the computing machinemay operate in the capacity of a server, a client, or both in server-client network environments. In an example, the computing machinemay act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. In this document, the phrases P2P, device-to-device (D2D) and sidelink may be used interchangeably. The computing machinemay be a specialized computer, a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile telephone, a smart phone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.

Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms. Modules and components are tangible entities (e.g., hardware) capable of performing specified operations and may be configured or arranged in a certain manner. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems/apparatus (e.g., a standalone, client or server computer system) or one or more hardware processors may be configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software may reside on a machine readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.

Accordingly, the term “module” (and “component”) is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operation described herein. Considering examples in which modules are temporarily configured, each of the modules need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose hardware processor configured using software, the general-purpose hardware processor may be configured as respective different modules at different times. Software may accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time.

500 502 504 506 508 504 500 510 512 514 510 512 514 500 516 518 520 521 500 528 The computing machinemay include a hardware processor(e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memoryand a static memory, some or all of which may communicate with each other via an interlink (e.g., bus). Although not shown, the main memorymay contain any or all of removable storage and non-removable storage, volatile memory or non-volatile memory. The computing machinemay further include a video display unit(or other display unit), an alphanumeric input device(e.g., a keyboard), and a user interface (UI) navigation device(e.g., a mouse). In an example, the display unit, input deviceand UI navigation devicemay be a touch screen display. The computing machinemay additionally include a storage device (e.g., drive unit), a signal generation device(e.g., a speaker), a network interface device, and one or more sensors, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The computing machinemay include an output controller, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

516 522 524 524 504 506 502 500 502 504 506 516 The drive unit(e.g., a storage device) may include a machine readable mediumon which is stored one or more sets of data structures or instructions(e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructionsmay also reside, completely or at least partially, within the main memory, within static memory, or within the hardware processorduring execution thereof by the computing machine. In an example, one or any combination of the hardware processor, the main memory, the static memory, or the storage devicemay constitute machine readable media.

522 524 While the machine readable mediumis illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions.

500 500 The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the computing machineand that cause the computing machineto perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. Specific examples of machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); and CD-ROM and DVD-ROM disks. In some examples, machine readable media may include non-transitory machine readable media. In some examples, machine readable media may include machine readable media that is not a transitory propagating signal.

524 526 520 520 526 The instructionsmay further be transmitted or received over a communications networkusing a transmission medium via the network interface deviceutilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, a Long Term Evolution (LTE) family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface devicemay include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network.

Energy consumption is a crucial consideration in edge computing, particularly in the context of edge machine learning (ML). Edge ML refers to the practice of running machine learning models on edge devices, which are typically closer to the source of data generation compared to cloud-based computing.

While the focus in the development of conventional ML models has traditionally been on accuracy and speed, energy efficiency becomes of utmost importance with ML models being deployed on edge devices, often times operating on battery power.

Energy efficiency is especially problematic in the context of tiny ML-a subset of edge ML, specifically targeting the most constrained devices such as microcontrollers or other embedded systems, which have very limited computational, memory, and energy resources.

Tiny ML models are designed to be highly efficient and compact, with optimized algorithms and architectures that can run locally on such devices. These models are typically trained on a larger host machine and then flashed to the constrained target device, where they are used for inference.

6 FIG. 600 602 604 606 608 610 612 614 616 614 618 616 615 616 618 602 612 614 616 618 illustrates an example hierarchyof ML classes. As shown, Complementary metal-oxide semiconductor (CMOS)/infrared (IR) cameras, optical, inertial measurement units (IMUs), audio microphones (mics)/mouth voice, environment/ecology, and physical/chemicalsensors feed to tiny ML. Edge MLis more advanced or complex than tiny ML. Cloud MLis more advanced or complex than edge ML. Tiny ML, edge ML, and cloud MLreceive input from the sensors-. Tiny MLuses the algorithm of convolutional neural network and hardware of microcontroller unit (MCU) with or without hardware accelerators. Edge MLuses optimized algorithms and convolutional neural networks (e.g., light-weight) and hardware of system on a chip (SoC) with neural processing unit (NPU)/neural signal processor (NSP) accelerators. Cloud MLuses the algorithm of deep neural network on the cloud and hardware of tensor processing unit (TPU), field-programmable gate array (FPGA), graphics processing unit (GPU), and/or central processing unit (CPU).

616 614 Some implementations are related to an inference energy estimation framework for edge MLdevices. Some implementations estimate energy consumption of tiny MLmodels inference on constrained edge devices given the architecture of the model and a type of device as inputs. Some implementations may assist in enabling developers to create and deploy machine learning models on small, low-power devices such as microcontrollers and sensors.

Some schemes leverage performance monitor counters (PMCs) and/or simulations to determine energy use of ML models executing on edge devices. Some disadvantages of these schemes include that PMCs do not provide per-processor results and simulations may utilize significant time overhead.

ML includes, among other things, building models to enable computers to “learn” from data. Different ML techniques and model types are applied to either classification tasks, where the ML model classifies an input sample into one of predefined categories, or regression tasks, where a model predicts or estimates a continuous value based on one or more input values.

A convolutional neural network (CNN) is a ML model that is used to extract features (like edges, shapes) from input images to classify them in one of categories. CNNs consist of different types of layers, mainly convolutional layers (e.g., pointwise convolution, depthwise convolution) and activation layers.

7 FIG. 700 702 704 706 illustrates an example convolution layer principle. As shown, an input feature mapis passed through a convolutional (conv) filterto obtain an output feature map.

Training and inference are two distinct phases in the life cycle of a machine learning model.

During the training phase, a machine learning model learns patterns and features from a labeled dataset. This involves adjusting the model's parameters through iterative optimization techniques to minimize the difference between predicted outputs and actual labels. Training typically uses a larger amount of computational resources and time compared to inference.

The inference phase involves applying the trained model to new, unseen data to make predictions or classifications. This phase may use less computational power compared to training and is often executed on devices with constrained resources, such as edge devices. More lightweight frameworks, which are typically subsets of training frameworks, are used for running inference on devices. Inference might, in some cases, not leverage operating system support, any standard C or C++ libraries, or dynamic memory allocation.

In the context of CNNs, training involves updating the weights of convolutional and other layers using backpropagation and gradient descent methods. Inference, however, consists of passing new data through the trained layers to obtain predictions without modifying the model's parameters.

Edge ML introduces a set of unique challenges and considerations that differentiate it from traditional machine learning paradigms.

Edge ML has limited resources. Machine learning algorithms that are efficient on traditional systems might not directly translate to embedded platforms due to computational resource constraints. The development of novel optimization techniques that strike a balance between model complexity and resource utilization may be useful.

In deployment and maintenance, unlike traditional setups where models can be updated centrally, edge devices might be deployed in remote or inaccessible locations. This introduces challenges related to model deployment, updates, and maintenance. Over-the-air updates, model version control, and adaptive learning techniques are useful in ensuring the models stay relevant and performant over time.

Turning to energy efficiency, machine learning algorithms optimized solely for accuracy might not be suitable in context of a battery-powered device, as they could drain the device's energy reserves rapidly. Developing energy-efficient algorithms that balance accuracy with power consumption is essential. Some implementations attempt to tackle this constraint by providing embedded ML engineers with a faster way to reason about an energy budget of the application they are developing.

8 FIG. 9 FIG. 800 800 802 804 806 808 810 800 500 802 502 804 520 526 804 804 900 illustrates an example of an edge device, in accordance with some embodiments. As shown, the edge deviceincludes processing circuitry, a communication interface, sensors, a power supply, and memory. The edge devicemay include all or a portion of the components of the computing machine. The processing circuitrymay correspond to the processor. The communication interfacemay correspond to the network interface deviceand may be used for communication over the network. Alternatively or in addition, the communication interfacemay correspond to a wired connection or a direct radio (e.g., Bluetooth®) connection. As shown, the communication interfaceis used to communicate with a computing device, which is described in greater detail in conjunction with.

806 521 806 806 806 800 806 800 806 812 800 812 810 The sensorsmay correspond to the sensors. The sensorsmay include at least one of a temperature sensor, a heat sensor, a pressure sensor, a light sensor, an ultraviolet or infrared sensor, a sound sensor, an ultrasound sensor, a radar or LIDAR (light detection and ranging) sensor, a motion sensor, or the like. While multiple sensorsare described, the disclosed technology may be implemented with a single sensor. The sensorsmay be components of the edge device, as shown. Alternatively, the sensorsmay be connected to the edge devicevia at least one of a wired connection, a wireless (e.g., Bluetooth®, Wi-Fi® or near field communication (NFC)) connection, or a network connection. The sensorsdetect streaming sensor data(e.g., environmental data of an environment surrounding the edge device) and transmit the streaming sensor datato the memoryfor storage or processing.

810 504 506 516 810 812 814 816 818 812 806 810 814 900 812 900 816 900 812 900 The memorymay correspond to at least one of the main memory, the static memory, or the drive unit. As illustrated, the memorystores the streaming sensor data, a trigger condition, a termination trigger, and a thin ML engine. As described above, the streaming sensor datais received from the sensorsand may be briefly stored in the memoryfor processing. The trigger conditionmay be received from the computing deviceand indicates a trigger for initiating transmission of at least a portion of the streaming sensor datato the computing device. The termination triggermay be received from the computing deviceand indicates a trigger for terminating the transmission of the portion of the streaming sensor datato the computing device.

818 818 812 800 818 814 816 816 818 818 800 900 The thin ML enginemay be a CNN or may implement other ML technology. The thin ML engineallows for some ML processing of the streaming sensor data(e.g., anomaly detection) to be performed at the edge device. In some cases, the output of the thin ML enginemay correspond to the trigger conditionor the termination trigger, or the trigger condition or the termination triggermay be based on values calculated by the thin ML engine. The thin ML enginemay be obtained, by the edge device, from the computing device.

800 814 810 800 800 812 806 800 812 814 800 804 812 900 814 According to some implementations, the edge devicestores the trigger conditionin the memoryof the edge device. The edge deviceaccesses streaming sensor datausing at least one of the sensors. The edge devicedetermines, based on the streaming sensor data, that the trigger conditionis met. The edge devicetransmits, using the communication interface, the streaming sensor datato the computing devicebased on determining that the trigger conditionis met.

814 812 812 812 812 812 In some cases, the trigger conditionincludes a value determined based on the streaming sensor datapassing (e.g., exceeding or falling below) a predefined threshold. The value may correspond to the streaming sensor data(e.g., the temperature going below 0 Celsius or going above 100 Celsius) or may correspond to a mathematical function of the streaming sensor data(e.g., the pressure in mmHg divided by the temperature in Kelvin being within a numeric range). In some cases, the trigger condition is based on a change in value determined based on the streaming sensor data(e.g., the temperature rising or falling at a rate exceeding 2 Celsius degrees per minute, where the streaming sensor dataincludes the temperature but not its rate of change).

818 818 804 900 900 900 In some cases, the trigger condition is based on the streaming sensor data itself and/or a value determined based on the streaming sensor data being in an anomaly range. The anomaly range may be identified by the thin ML engine. For example, the thin ML enginemay apply statistical and/or CNN techniques to determine the anomaly range. Alternatively, the anomaly range may be received, via the communication interface, from the computing device. The anomaly range may be manually determined by a user of the computing deviceor may be determined by statistical or artificial intelligence techniques implemented at the computing deviceand/or another computing device.

818 800 814 812 800 814 900 In some cases, the thin ML engineis a classification engine that executes at the edge device. The trigger conditionis based on a classifier result determined based on the streaming sensor data. In some cases, the edge devicereceives a representation of a set of classifier results associated with the trigger conditionfrom the computing device.

814 812 814 818 812 814 812 In some cases, the trigger conditionincludes a value from the streaming sensor dataentering a range (e.g., the temperature exceeding 100 Celsius). In some cases, the trigger conditionis based on a classifier result (e.g., calculated by the thin ML engine) determined based on the streaming sensor data. In some cases, the trigger conditionis based on an average, a root mean square, or a moving average of values in the streaming sensor datareceived during a predetermined period of time preceding a current time.

816 800 816 816 812 816 814 816 900 816 808 800 800 816 800 812 812 812 As illustrated, the termination triggeris stored at the edge device. The edge device terminates transmission of the streaming sensor data in response to determining that the termination triggeris met. The termination triggermay correspond to the streaming sensor dataand/or be determined based on the streaming sensor data, similarly to the trigger condition. Alternatively, the termination triggermay correspond to a receipt of a termination signal from the computing deviceor another machine. In some cases, the termination triggercorresponds to the battery power of the power supplyof the edge devicefalling below a threshold level. Some implementations might not involve the edge devicestoring the termination trigger. The edge devicemay use other techniques to determine when to terminate transmission of the streaming sensor data. For example, the streaming sensor datamay be transmitted for a predetermined time period (e.g., two minutes) after occurrence of the trigger condition.

9 FIG. 900 900 900 900 900 902 904 906 900 500 902 502 904 520 526 904 904 800 illustrates an example of the computing device, in accordance with some embodiments. While the computing deviceis illustrates as a single device. The computing devicemay include multiple devices (e.g., a studio of devices or a server farm). The computing devicemay include one or more of a server, a laptop computer, or a desktop computer. As shown, the computing deviceincludes processing circuitry, a communication interface, and a memory. The computing devicemay include all or a portion of the components of the computing machine. The processing circuitrymay correspond to the processor. The communication interfacemay correspond to the network interface deviceand may be used for communication over the network. Alternatively or in addition, the communication interfacemay correspond to a wired connection or a direct radio (e.g., Bluetooth®) connection. As shown, the communication interfaceis used to communicate with the edge device.

906 504 506 516 906 908 908 812 800 900 812 800 900 800 The memorymay correspond to at least one of the main memory, the static memory, or the drive unit. As shown, the memorystores streaming sensor data. The streaming sensor datamay correspond to a portion of the streaming sensor datathat is transmitted from the edge deviceto the computing device. The portion of the streaming sensor datathat is transmitted from the edge deviceto the computing devicemay be determined, by the edge device, using the techniques described herein.

908 910 910 910 908 As illustrated, the streaming sensor datais provided to a data processing engine. The data processing enginemay be an artificial intelligence engine and may include at least one of a statistical engine, a CNN, a large language model (LLM), a generative pretrained transformer (GPT), or other artificial intelligence or data processing technology. The data processing engineobtains intelligence from the streaming sensor dataand provides the obtained intelligence to human users or other computing machines for further analysis or action taking. The human users may receive the obtained intelligence in a visual output (e.g., via a graphical user interface) or in a message transmitted to an address (e.g., an email address or other messaging address). The message may be written in a natural language (e.g., English, Spanish, or Japanese) using at least one of the LLM or the GPT.

906 912 912 914 800 904 914 814 816 818 818 912 914 800 800 914 814 816 As shown, the memoryincludes an edge device control engine. The edge device control enginedetermines one or more edge device control datafor transmission to the edge devicevia the communication interface. The edge device control datamay include at least one of the trigger condition, the termination trigger, the thin ML engine, or values for operation of the thin ML engine. In some cases, the edge device control engineis an artificial intelligence engine that automatically determines the edge device control data(e.g., based on stored data about the edge deviceor the environment in which the edge deviceis operating). Alternatively, the edge device control datamay be determined directly by human input or based on a human input. For example, the human input may correspond to a sensitivity value (e.g., indicating relative tolerance for false negative and false positive results), and thresholds for the trigger conditionor the termination triggermay be determined based on the sensitivity value.

10 FIG. 1000 1000 illustrates an example of a systemfor trigger-based data ingestion for machine learning, in accordance with some embodiments. The systemmay be used for data logging, data transmission, and/or data processing, as described herein.

1000 1002 812 1004 1002 806 1004 800 1004 1006 1008 1008 1010 1008 1012 1014 As shown, the systemincludes a sensorthat provides streaming sensor data (e.g., the streaming sensor data) to an ingestion device. The sensormay be one or more of the sensors. The ingestion devicemay correspond to the edge device. As shown, the ingestion devicehas a universal asynchronous receiver-transmitter (UART) or universal serial bus (USB) connectionto a data logger. The data loggertransmits the received sensor data to flash storage. The data loggertransmits the received sensor data, via a USB or Wi-Fi® connection, to a computing studio.

900 1008 1010 1014 1008 800 1004 1008 1004 1010 1014 1008 1004 1008 1004 1006 The computing devicemay correspond to one or more of the data logger, the flash storage, or the computing studio. Alternatively, the data loggermay be a component of the edge device. In some cases, the ingestion deviceis in a remote location (e.g., in an agricultural field or in a rarely visited location (e.g., deep sea or Antarctica) being studied by scientists). Thus, the data loggeris used to obtain data from the ingestion deviceand to transmit the data to the flash storageand/or to the computing studio. In some cases, the data loggeris a mobile phone (or other portable computing device) that is occasionally taken to the remote location to communicate with the ingestion device. In some cases, the data loggeris a special-purpose data logging device that is not a mobile phone and that is capable of communication with the ingestion devicevia the UART or USB connection.

1004 800 In some cases, users of edge devices (e.g., the ingestion deviceor the edge device) do not use ingestion tools for data capturing. Instead, such users may create custom tools and load sensor data using a comma separated value (CSV) uploader. It may be difficult to sample data from a device that is not physically connected to a laptop computer or desktop computer doing the sampling.

1008 1008 1008 1012 1008 1010 In some cases, the data loggermay have battery power and wireless connectivity. In some cases, the data loggermay have high data throughput, being capable of high-speed USB data transmission. In some cases, the data loggeris capable of sampling over a long period of time (e.g., a time period longer than a threshold, for example, longer than one hour, longer than two hours, longer than one day, longer than two days, or the like) without being connected to the computing studio. The data loggermay be equipped with the flash storagehaving sufficient free space for the sampling over the long period of time.

1010 1010 1014 1008 1014 1008 In some cases, the flash storageis a removable storage medium (e.g., a USB flash drive) that may be disconnected from the data loggerand connected to a device of the computing studio. In some cases, the data loggerstarts and/or stops logging data based on a control signal received from the computing studio. Alternatively, the starting and/or stopping of logging data may be done without connectivity (e.g., by a human user pushing a button of the data logger).

1004 1008 1004 1008 1004 1014 1008 1004 1004 1008 If the ingestion deviceis not capable of storing data, the data loggermay be used to store data obtained by the ingestion device. The data loggermay communicate with the ingestion deviceto obtain sensor data and to provide the sensor data to the computing studio. The data loggeracts as a USB host or connects over UART to the ingestion device. The ingestion deviceoutputs the sensor data in a specialized format for capturing and storing by the data logger.

1008 1010 1014 1014 The data loggermay store the sampling data in a specialized format (e.g., concise binary object representation (CBOR) format) as a file on an secure digital (SD) card or on the flash storage. Upon connection with the computing studio, the data logger may communicate with the computing studioto synchronize the sampled data.

1002 Capture relevant data from the sensorcan take up hours of running ingestion. This may unnecessarily take up resources (e.g., memory or power). Furthermore, a human user might have to review the data and find out if data of interest is captured.

1014 Using a threshold trigger, some implementations start capturing sampled data when the signal passes a pre-defined threshold. This works on time series data. An averaged value of the axis with the highest output value may be used for the trigger. Averaging can be done using Root Mean Square (RMS), or another technique. The threshold may be configurable in the computing studio.

Using a data change trigger, instead of threshold value that needs to be reached before the sampling start, the start of sampling may be triggered by a change of value of any of the axes. A hysteresis value may be configured to determine the minimal step size.

1004 1014 818 Using an anomaly trigger, some implementations use the inference pipeline (which might not be used during ingestion) to trigger sampling. This way, some implementations can capture relevant data with same power and memory budget. A user first creates a model with known (idle) data and an anomaly learning block. This model is deployed on the ingestion device. From the computing studio, the user may select an anomaly trigger using a user interface for controlling the ingestion (e.g., an ingestion tab). This user interface may allow the user to enable sampling if the anomaly score is higher than a preconfigured value. In some cases, the anomaly score may be based on a visual anomaly determined by a CNN (e.g., of the thin ML engine).

818 1014 The classifier trigger operates similarly to the anomaly trigger but uses classifier results (e.g., obtained by the thin ML engine) instead. The score threshold and/or the label of the category of interest may be configured by users of the computing studio.

1014 818 1014 Some implementations relate to configuring a trigger to start and/or stop data ingestion. The trigger may be at least one of a threshold trigger, an anomaly trigger, or a classifier trigger. The threshold trigger may be the RMS level being above (or below) a certain threshold or being within at least one range. This triggers sampling to start. Sampling stops when the RMS falls below (or above) the threshold or leaves the at least one range. The anomaly trigger allows the user to select the anomaly threshold via the computing studio. The classifier trigger causes sampling to start if a certain class is detected by a classifier (e.g., the thin ML engine). The label and/or the threshold for the classifier are set in the computing studio.

1014 In some cases, available trigger options may be presented to a user of the computing studiovia a dropdown menu, a set of radio buttons, or another graphical user interface element. In some cases, at least one of the anomaly trigger, the threshold trigger, or the classifier trigger might not be available.

11 FIG. 1100 1100 800 is a flowchart of an example techniquefor trigger-based data ingestion for machine learning, in accordance with some embodiments. The techniquemay be preformed by an edge device (e.g., the edge device).

1102 814 At block, the edge device stores a representation of a trigger condition (e.g., the trigger condition). The trigger condition may include least one of the anomaly trigger, the threshold trigger, or the classifier trigger.

1104 812 806 1002 At block, the edge device accesses streaming sensor data (e.g., the streaming sensor data). The streaming sensor data may be received from sensors of the edge device or sensors connected to the edge device. The sensors may correspond to the sensorsand/or the sensor.

1106 At block, the edge device determines, based on the streaming sensor data, that the trigger condition is met. For example, the edge device may continuously or periodically compare the streaming sensor data (or a calculation based on the streaming sensor data) to the trigger condition to determine if the trigger condition is met.

1108 900 1004 1010 1014 At block, the edge device transmits the streaming sensor data to a computing device (e.g., the computing device, the ingestion device, the flash, or the computing studio) in response to determining that the trigger condition is met. The transmitted streaming sensor data may be displayed as a graphical output at the computing device and/or further processed at the computing device using more complex models than those available at the edge device.

Example 1 is a method comprising: storing a representation of a trigger condition at an edge device comprising processing circuitry and memory; accessing streaming sensor data at the edge device; determining, based on the streaming sensor data and using the processing circuitry of the edge device, that the trigger condition is met; and transmitting the streaming sensor data from the edge device to a computing device in response to determining that the trigger condition is met. In Example 2, the subject matter of Example 1 includes, wherein the trigger condition comprises a value determined based on the streaming sensor data passing a predefined threshold. In Example 3, the subject matter of Examples 1-2 includes, wherein the trigger condition is based on a change in a value determined based on the streaming sensor data. In Example 4, the subject matter of Examples 1-3 includes, wherein the trigger condition is based on a value determined based on the streaming sensor data being in an anomaly range, wherein the anomaly range is identified by a thin machine learning engine executing at the edge device. In Example 5, the subject matter of Example 4 includes, receiving, from the computing device, a representation of the anomaly range. In Example 6, the subject matter of Examples 1-5 includes, wherein the trigger condition is based on a classifier result determined based on the streaming sensor data, wherein the classifier result is determined by a thin classification engine executing at the edge device. In Example 7, the subject matter of Example 6 includes, receiving, from the computing device, a representation of a set of classifier results associated with the trigger condition. In Example 8, the subject matter of Examples 1-7 includes, wherein the trigger condition is based on an average, a root mean square, or a moving average of values in the streaming sensor data received during a predetermined period of time preceding a current time. In Example 9, the subject matter of Examples 1-8 includes, storing a termination trigger condition at the edge device; terminating transmission of the streaming sensor data in response to determining that the termination trigger condition is met. In Example 10, the subject matter of Examples 1-9 includes, wherein the streaming sensor data is transmitted for a predetermined time period. In Example 11, the subject matter of Examples 1-10 includes, wherein a memory capacity of the memory of the edge device is below a threshold memory capacity. In Example 12, the subject matter of Examples 1-11 includes, wherein a processing capacity of the processing circuitry of the edge device is below a threshold processing capacity. Example 13 is a non-transitory computer-readable medium storing instructions operable to cause an edge device to perform operations comprising: storing a representation of a trigger condition at the edge device comprising processing circuitry and memory; accessing streaming sensor data at the edge device; determining, based on the streaming sensor data and using the processing circuitry of the edge device, that the trigger condition is met; and transmitting the streaming sensor data from the edge device to a computing device in response to determining that the trigger condition is met. In Example 14, the subject matter of Example 13 includes, wherein the trigger condition comprises a value determined based on the streaming sensor data passing a predefined threshold. In Example 15, the subject matter of Examples 13-14 includes, wherein the trigger condition is based on a change in a value determined based on the streaming sensor data. In Example 16, the subject matter of Examples 13-15 includes, wherein the trigger condition is based on a value determined based on the streaming sensor data being in an anomaly range, wherein the anomaly range is identified by a thin machine learning engine executing at the edge device. In Example 17, the subject matter of Example 16 includes, the operations further comprising: receiving, from the computing device, a representation of the anomaly range. Example 18 is an edge device comprising: memory storing instructions; and processing circuitry configured to execute the instructions to perform operations comprising: storing a representation of a trigger condition at the edge device; accessing streaming sensor data at the edge device; determining, based on the streaming sensor data and using the processing circuitry of the edge device, that the trigger condition is met; and transmitting the streaming sensor data from the edge device to a computing device in response to determining that the trigger condition is met. In Example 19, the subject matter of Example 18 includes, wherein the trigger condition comprises a value determined based on the streaming sensor data passing a predefined threshold. In Example 20, the subject matter of Examples 18-19 includes, wherein the trigger condition is based on a change in a value determined based on the streaming sensor data. Example 21 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-20. Example 22 is an apparatus comprising means to implement of any of Examples 1-20. Example 23 is a system to implement of any of Examples 1-20. Example 24 is a method to implement of any of Examples 1-20. Some embodiments are described as numbered examples (Example 1, 2, 3, etc.). These are provided as examples only and do not limit the technology disclosed herein.

As used herein, unless explicitly stated otherwise, any term specified in the singular may include its plural version. For example, “a computer that stores data and runs software,” may include a single computer that stores data and runs software or two computers-a first computer that stores data and a second computer that runs software. Also “a computer that stores data and runs software,” may include multiple computers that together stored data and run software. At least one of the multiple computers stores data, and at least one of the multiple computers runs software.

As used herein, the term “computer-readable medium” encompasses one or more computer-readable media. A computer-readable medium may include any storage unit (or multiple storage units) that store data or instructions that are readable by processing circuitry. A computer-readable medium may include, for example, at least one of a data repository, a data storage unit, a computer memory, a hard drive, a disk, or a random access memory. A computer-readable medium may include a single computer-readable medium or multiple computer-readable media. A computer-readable medium may be a transitory computer-readable medium or a non-transitory computer-readable medium.

As used herein, the term “memory” or “memory subsystem” includes one or more memories, where each memory may be a computer-readable medium. A memory subsystem may encompass memory hardware units (e.g., a hard drive or a disk) that store data or instructions in software form. Alternatively or in addition, the memory subsystem may include data or instructions that are hard-wired into processing circuitry. The memory subsystem may include a single memory unit or multiple joint or disjoint memory units, which each of the multiple joint or disjoint memory units storing all or a portion of the data described as being stored in the memory subsystem.

As used herein, processing circuitry includes one or more processors. The one or more processors may be arranged in one or more processing units, for example, a central processing unit (CPU), a graphics processing unit (GPU), or a combination of at least one of a CPU or a GPU.

As used herein, the term “engine” may include software, hardware, or a combination of software and hardware. An engine may be implemented using software stored in the memory subsystem. Alternatively, an engine may be hard-wired into processing circuitry. In some cases, an engine includes a combination of software stored in the memory subsystem and hardware that is hard-wired into the processing circuitry.

As used herein, the term “and/or” encompasses its plain and ordinary meaning and may refer to an intersection or a union of sets of data. For example, the phrase “A and/or B” encompasses the union of A and B. The phrase “A and/or B” encompasses the intersection of A and B. The phrase “A and/or B” encompasses at least one of A or at least one of B. The phrase “A and/or B” may encompass A alone, B alone, or A and B together.

Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, user equipment (UE), article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. § 1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.